Project Summary/Abstract Cardiac Magnetic Resonance (CMR) provides arguably the most comprehensive evaluation of the cardiovascular system; however, respiratory motion continues to adversely impact CMR, causing artifacts that lead to poor image quality, repeated scans, and decreased throughput, and thus represents a significant obstacle to clinical utility. For single-shot CMR, cardiac and breathing motions are ?frozen? by limiting the acquisition to an end-diastolic window less than 200 ms. For first pass perfusion, breathing motion cannot be eliminated because data from 50 to 60 consecutive heartbeats are required to capture contrast dynamics. For other single-shot applications such as late gadolinium enhancement (LGE) and parameter mapping, respiratory motion is introduced when the acquisition is repeated across several heartbeats to improve spatial and temporal resolution. To eliminate respiratory motion from single-shot images, non-rigid motion correction (MOCO) has been promoted as an attractive option that provides 100% acquisition efficiently. MOCO can be used either after the reconstruction or during the reconstruction. Such techniques, however, cannot account for through-plane motion, which can only be corrected prospectively, and can fail depending on image quality and the extent of motion. Prospective compensation of the respiratory motion has been recognized as an attractive alternative to existing gating and MOCO methods. Proposed methods use one or more navigator echoes?incompatible with or inefficient for many CMR protocols?to capture the respiratory motion and rely on simple parametric models that are inadequate to describe complex respiratory-induced cardiac motion. Due to these limitations, prospective methods have found limited applicability even in research settings. We propose a new framework to prospectively compensate respiratory motion. The proposed method, called PROspective Motion compensation using Pilot Tone (PROMPT), employs Pilot Tone technology and leverages machine learning principles to first learn complex respiratory-induced cardiac motion on a patient-specific basis and then prospectively compensate the motion by tracking the imaging plane, in real time, as a function of a Pilot Tone based respiratory signal. If successful, this synergistic combination of Pilot Tone and machine learning will lead to 100% efficiency for single-shot CMR exams performed under free-breathing conditions, will eliminate the need to setup navigator echoes, respiratory bellows, or other inefficient prospective gating measures, will minimize through-plane motion that can render the images non-diagnostic for CMR applications including fast-pass perfusion, parameter mapping, LGE, and coronary angiography, will provide a reliable surrogate measure of respiratory motion, and will facilitate highly accelerated compressive recovery.